[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120324-en":3,"doc-seo-120324-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120324,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","A scoping review of advancements in machine learning for glaucoma: current trends and future direction","A scoping review summarizes advances in machine learning for glaucoma, focusing on early diagnosis, disease progression prediction, treatment-response evaluation, and the development of personalized management strategies. The review aims to map the current research landscape, highlight development directions, and identify improvement trends by applying a scoping review framework aligned with PRISMA-ScR. A comprehensive Web of Science search supports analysis of 3,581 articles and extraction of key topic categories and future research signals.","TYPE Original Research PUBLISHED 24 April 2025  \nDOI 10. 3389/fmed.2025.1573329  \nOPEN ACCESS  \nEDITED BY  \nWeihua Yang,  \nSouthern Medical University, China  \nREVIEWED BY  \nLiang Zhao,  \nKyoto University, Japan Murugesan Raju,  \nUniversity of Missouri, United States  \n*CORRESPONDENCE  \nTing Fan  \n [tfan@cmu.edu.cn](tfan@cmu.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 08 February 2025  \nACCEPTED 03 April 2025  \nPUBLISHED 24 April 2025  \nCITATION  \nZhang J, Tian B, Tian M, Si X, Li J and Fan T (2025) A scoping review of advancements in machine learning for glaucoma: current trends and future direction.  \nFront. Med. 12:1573329 .  \ndoi: 10.3389/fmed.2025.1573329  \nCOPYRIGHT  \n© 2025 Zhang, Tian, Tian, Si, Li and Fan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA scoping review of advancements in machine learning for glaucoma: current trends and future direction  \nJiatong Zhang1†, Bocheng Tian2†, Mingke Tian3†, Xinxin Si4 , Jiani Li1 and Ting Fan5*  \n1The First Clinical Medical School, China Medical University, Shenyang, China, 2The Second Clinical Medical School, China Medical University, Shenyang, China, 3 Emory College of Arts and Sciences, Emory University, Atlanta, GA, United States, 4The Fourth Clinical Medical School, China Medical University, Shenyang, China, 5 School of Intelligent Medicine, China Medical University, Shenyang, China  \nIntroduction: Machine learning technology has demonstrated significant potential in glaucoma research, particularly in early diagnosis, predicting disease progression, evaluating treatment responses, and developing personalized treatment strategies. The application of machine learning not only enhances the understanding of the pathological mechanism of glaucoma and optimizes the diagnostic process but also provides patients with accurate medical services.  \nMethods: This study aimed to describe the current state of research, highlight directions for further development, and identify potential trends for improvement. This review was conducted following the scoping review of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension to showcase advancements in the application of machine learning in glaucoma research and treatment.  \nResults: We employed a comprehensive search strategy to retrieve literature from the Web of Science Core Collection database, ultimately including 3,581 articles in the analysis. Through data analysis, we identified current research hotspots, noted di􀀀erences in researchers’ attitudes and opinions, and predicted potential future development trends.  \nDiscussion: We divided the research topics into six categories, clearly identifying“eye diseases”, “retinal fundus imaging” and “risk factors” as the key terms for the development of this field. These findings signify the promising prospects of machine learning, particularly when integrated with multimodal technologies and large language models, to enhance the diagnosis and treatment of glaucoma.  \nKEYWORDS  \nmachine learning, glaucoma diagnosis, deep learning, multimodal imaging, ophthalmic research  \n1 Introduction  \nGlaucoma, characterized by retinal nerve 􀀂ber layer defects and concomitant visual 􀀂eld (VF) damage, is considered one of the leading causes of irreversible visual impairment worldwide (1) . Glaucoma is estimated to a􀀓ect more than 110 million individuals over the next two decades (2, 3) . Therefore, adopting novel diagnostic techniques to explore glaucoma pathogenesis and facilitate early detection protocols is of signi􀀂","cbCaitstzv7IN6zJ","https://ap.wps.com/l/cbCaitstzv7IN6zJ","pdf",1778307,1,12,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"What is the main purpose of this scoping review?\",\"answer\":\"To describe the current state of machine learning research for glaucoma, highlight directions for further development, and identify potential future improvement trends.\"},{\"question\":\"How was the literature for the review collected and analyzed?\",\"answer\":\"The review used a comprehensive search strategy in the Web of Science Core Collection database and included 3,581 articles for analysis.\"},{\"question\":\"Which research themes are emphasized in the review’s discussion?\",\"answer\":\"The discussion groups topics into six categories and identifies “eye diseases,” “retinal fundus imaging,” and “risk factors” as key 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